Method of detecting and quantifying sun-drying crops using satellite derived spectral signals

Abstract

The unique methodology and utility seeking patient protection in this application is using satellite imagery to quantify and derive harvest statistics of sun-drying crops. High resolution imagery can be used to geospatially define known coffee drying basins however it is impractical for continuous observation as it is costly and infrequent. The geospatially defined regions of interests of coffee drying basins can be matched with more frequent, lower resolution, multispectral satellite imagery (such as Sentinel-2). The signals can be tested against the known spectral signatures of washed and unwashed coffee to determine whether or not each pixel contains coffee. The result will yield a classified region of interest which can be used to determine the quantity of drying coffee and the washed to unwashed ratio of a harvest. With regular monitoring across multiple temporal scenes the harvest's seasonality and historical change can be derived.

I claim: 
     
       1. A method of assessing a harvest of sun drying crops, comprising:
 capturing a multispectral satellite or an aerial image of a coffee estate sun-drying beans including areas where coffee is sun-dried; 
 defining a geospatial region of interest within the image; 
 calculating a Normalized Difference Vegetation Index and a Normalized Difference Water Index for each pixel within the geospatial region of interest; 
 testing each pixel within the geospatial region of interest for a reflectance value matching at least one spectral signature including washed or unwashed coffee dries; 
 classifying each pixel within the geospatial region of interest based on the spectral signature and the calculated Normalized Difference Vegetation Index and the Normalized Difference Water Index; 
 comparing temporal changes of the Normalized Difference Vegetation Index and the Normalized Difference Water Index for each classified pixel within the geospatial region of interest; 
 analyzing a harvest cycle and an amount of crop drying based on the spectral signature, the Normalized Difference Vegetation Index and the Normalized Difference Water Index for each of the classified pixel within the geospatial region of interest; 
 counting the classified pixels to monitor and quantify the amount of crop dying and the harvest cycle; 
 assessing the coffee estate sun-drying beans after monitoring and quantifying the amount of crop dying and the harvest cycle; and 
 deriving a regional or global coffee productivity levels using the assessing of the coffee estate sun drying beans and comparing the productivity levels across a sample of farms. 
 
     
     
       2. The method according to  claim 1  comprising:
 testing and classifying each pixel against a correlation of decreasing Normalized Difference water Index and increasing Normalized Difference Vegetation Index as containing unwashed beans drying. 
 
     
     
       3. The method of  claim 2 , wherein the classification of each pixel includes areas both inside and outside of the geospatial region of interest.